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Unit of Study COMP6001 - Computational Intelligence and Machine Learning (2021)

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Unit Snapshot

  • Enrolment information

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Unit description

Introduces students to machine learning. Students will take an algorithmic approach to machine learning in which real-world problems will be solved through machine learning techniques. Students will familiarise themselves with a wide range of algorithms and implement them for problem solving in Python/Octave.

Unit content

Topic 1: Intro to machine learning

Topic 2: Types of machine learning

Topic 3: Regression and prediction

Topic 4: Classification

Topic 5: Neural networks

Topic 6: Machine learning: best practices

Topic 7: Philosophy of machine learning

 

Availabilities

Location Domestic International
Gold Coast
Session1,3
Session1,3
Melbourne N/A
Session1
Online
Session1,3
Session1,3
Perth N/A
Session1
Sydney N/A
Session1

Learning outcomes

Unit Learning Outcomes express learning achievement in terms of what a student should know, understand and be able to do on completion of a unit. These outcomes are aligned with the graduate attributes. The unit learning outcomes and graduate attributes are also the basis of evaluating prior learning.

On completion of this unit, students should be able to:

identify and evaluate the evolution and recent trends in computational intelligence and machine learning

critically analyse real-world machine learning problems and compare between a range of solutions in a variety of contexts

develop, create and implement machine learning solutions to complicated problems

plan and implement parts of the tasks in a machine learning pipeline to complete a predictive analysis problem to satisfaction

Prescribed learning resources

Prescribed Texts
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Prescribed Texts
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Prescribed Learning Resources may change in future Teaching Periods.